Poder do Teste (1 − β)
- Created by
- Renato Passos, Eng. de Software
- Reviewed by
- Renato Passos, Eng. de Software
Last updated: Apr 18, 2026
Formula
poder
About this calculator
The Power of the Test calculator is a useful tool for estimating the probability of rejecting a null hypothesis in a statistical test, considering the risk of type II error (β). With this calculator, you can calculate the power of a statistical test, which is an important measure in research studies.
The power of a statistical test is calculated using the formula: Power = 1 - β, where β is the risk of type II error. This means that, the smaller the risk of type II error, the greater will be the power of the test.
The Power of the Test calculator is especially useful in research studies where the goal is to detect significant effects of a treatment or intervention. Additionally, it can be used to calculate the sample size required to achieve a given test power.
Remember that the power of a statistical test is affected by several factors, including the sample size, the variance of the error and the significance level. Therefore, it is essential to consider these factors when calculating the power of a test.
Frequently asked questions
What is the power of a statistical test?
The power of a statistical test is the probability of rejecting the null hypothesis in a statistical test, considering the risk of type II error (β).
How to calculate the power of a statistical test?
The power of a statistical test is calculated using the formula: Power = 1 - β, where β is the risk of type II error.
When to use the Power of the Test calculator?
Use the Power of the Test calculator in research studies where the goal is to detect significant effects of a treatment or intervention.
What affects the power of a statistical test?
The sample size, the variance of the error and the significance level affect the power of a statistical test.
What are the advantages of the Power of the Test calculator?
The advantages of the Power of the Test calculator include the ease of calculating the power of a statistical test and the possibility of calculating the sample size required to achieve a given test power.